What data in this category looks like
Data primarily comes from school qualification documents, teacher filing materials, curriculum system documents, student service records, and annual compliance self-assessment reports submitted by education service institutions. Updates trigger when institutional qualifications change, curricula iterate, or annual audit deadlines arrive, with no fixed cycle. Most documents pair structured fields with unstructured attachments. Fields include institutional uniform identification numbers, teacher qualification levels, course durations, service coverage population ranges, and more. Each report’s main body holds multiple independent information modules with wide variation in span.
What constraints do these characteristics impose on vector models and indexing?
The structured fields in education service due diligence reports have a high proportion and carry clear identifiers and units. The vectorization process must distinguish processing logic for structured metadata and unstructured main text to avoid semantic confusion. Document updates trigger at non-fixed nodes, creating demand for batch updates. The indexing system must support incremental synchronization to cut resource use from full index rebuilding. Individual reports have long main text spans; when splitting long text modules, preserve module boundaries to avoid semantic breaks across modules. Include unit information attached to fields in the vectorization context to ensure accurate retrieval of compliant data with matching units.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Education due diligence reports include long-text curriculum systems and teacher resumes. This range preserves the semantic integrity of modules, avoids semantic breaks caused by too short chunks, and excessive chunk length increases retrieval redundancy |
chunk_overlap | 100–150 characters | Contextual coherence must be retained after long text is segmented to ensure semantic continuity across segments, adapting to retrieval of coherent content such as teacher resumes and course outlines |
vector_model | bge-m3 or multilingual vector models of comparable scale | Education due diligence reports contain multiple types of fields. This model has balanced semantic representation capabilities for structured metadata and unstructured main text, adapting to retrieval needs for multi-module content |
index_retrieve_topk | Top 8–12 results | The retrieval needs for education due diligence reports mostly focus on precise matching of specific fields such as qualifications and courses. Too many retrieved results increase subsequent screening costs, while too few may miss key information |
incremental_index_enable | Enabled | Updates to due diligence reports from education service institutions are mostly triggered in batches. Incremental indexing reduces repeated computing resource consumption, adapting to the update rhythm of non-fixed cycles |
metadata_vector_weight | 0.3–0.5 | Structured metadata (such as qualification numbers, course durations) has a significant impact on retrieval accuracy. A reasonable weight must be assigned to balance the semantic matching degree between metadata and main text |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: When creating a knowledge base, the text understanding model dropdown does not display the added ollama qwen2.5 and bge-m3 models. Cause: The models were not correctly bound to the dedicated groups for vector models and text understanding models, or the model type does not match the fields required by the interface.
- Phenomenon: When retraining a vector model, only single-file operations are supported, and batch processing of all uploaded datasets is not possible. Cause: The batch index retraining configuration item was not enabled, or the system did not preset scheduling rules for batch tasks, resulting in only single-file processing logic being triggered.
- Phenomenon: Retrieval results after vectorization show field confusion between different units of the same type, such as cross-matched results between course duration and service duration. Cause: Semantic weights were not separately configured for structured fields, or unit-associated context information was not retained during the vectorization process, leading to deviations in semantic representation.
How to confirm the configuration is correct
- Upload a sample education due diligence report that includes complete structured fields and long-text modules, and check whether the segmented content retains module boundaries and unit-associated information.
- Trigger a batch update task, and verify that the indexing system only synchronizes and processes newly added or modified files, without performing a full rebuild of the entire index library.
- Initiate a retrieval request for a specific field, and verify that the semantic matching degree of the retrieved results meets business expectations.
- Check the system binding logs to confirm that the configured vector models and text understanding models are correctly associated with the configuration group of the current knowledge base.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.